Model profile
OpenAI

OpenAI: o4 Mini

OpenAI: o4 Mini is a budget-priced multimodal generalist from OpenAI with heavy runtime profile, large context posture, and the clearest fit around long-context research / multimodal.

Best for: Long-context research / MultimodalHeavy latencyLarge contextBudget pricing
Intelligence
33.1

Benchmark blend

Coding
25.6

Dev workflow signal

Context
200K Tokens

Large

Input Price
$1.10

Budget tier

Decision snapshot
51

OpenAI: o4 Mini currently reads as a budget multimodal option with large context and a heavy runtime profile.

Overall profile
Use-case specific
Best for
Long-context research / Multimodal
Latency tier
Heavy
Price tier
Budget
Source coverage
OpenRouterArtificial AnalysisVision signal

Decision Strip

Decision rail before the raw tables

Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.

Intelligence
33.1
33

General reasoning and benchmark headroom.

Limited
Speed
120 tok/s
43

TTFT 24.35s

Situational
Context
200K Tokens
88

How much prompt and task state can stay in view.

Above average
Price
$1.10
86

$4.40 output / 1M

Efficient

Editorial Profile

OpenAI: o4 Mini in one narrative

Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.

Use-case specificCoding score 26Math score 91Vision enabled

OpenAI o4-mini is a compact reasoning model in the o-series, optimized for fast, cost-efficient performance while retaining strong multimodal and agentic capabilities. It supports tool use and demonstrates competitive reasoning...

Identity

OpenAI multimodal profile

Positioning

Long-context research / Multimodal with large context and heavy runtime.

Cost posture

Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.

Strengths
  • Large context headroom supports repo-wide prompts and long research sessions.

  • Vision-capable routing opens up multimodal review and extraction workflows.

Tradeoffs
  • Budget-friendly input pricing is a strength, but raw capability may vary by workload.

  • Latency profile is better for deliberate runs than rapid back-and-forth chat.

Best fit
  • Image-grounded review, multimodal extraction, and UI audit workflows.

  • Long-context summarization, repo analysis, and policy or document review.

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Context
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Benchmarks

Grouped by job-to-be-done

Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.

General intelligence

Broad reasoning, knowledge depth, and flagship benchmark posture.

Intelligence Index
33.1
MMLU Pro
83.2%
GPQA
78.4%
HLE
17.5%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
25.6
LiveCodeBench
0.859
SciCode
46.5%
Math

Formal reasoning, structured problem solving, and competition-style math.

Math Index
90.7
AIME
94.0%
AIME 2025
90.7%
Math 500
98.9%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
68.7%
TAU2
55.6%
TerminalBench Hard
15.2%
LCR
55.0%

Specs & Pricing

Technical snapshot and cost posture

Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.

Technical snapshot
Context Window
200K Tokens
Vision
Enabled
Modalities
file, image, text
Tokenizer
GPT
Max Completion
100000
Moderation
Yes
Supported Parameters
include_reasoningmax_tokensreasoningresponse_formatseedstructured_outputstool_choicetools
Input Modalities
fileimagetext
Output Modalities
text
Price architecture
Input
per 1M input tokens
$1.10
Output
per 1M output tokens
$4.40
Blended
AA 3:1 mix
$1.93

This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.

OR Web Search Price
$0.0100
OR Cache Read
$0.00

Metadata

Raw source tables at the end

Verification details remain available, but the page no longer forces them ahead of the editorial read.